Multivariate Poisson-Lognormal Models for Predicting Peak-Period Crash Frequency of Joint On-Ramp and Merge Segments on Freeways

Abstract

Because of a growing crash occurrence in conflict areas, the ramp and merge segments on freeways are a concern for transportation researchers and practitioners. Therefore, short-term safety-performance functions (SPFs) have been proposed to predict crash frequency at a.m. and p.m. peak-period aggregation levels using microscopic traffic-detector data. The proposed short-term crash prediction models could achieve more accuracy and flexibility, give a better understanding of how safety evaluations change over time, and enable the taking of appropriate actions. This study contributes to the literature by using the multivariate Poisson-lognormal (MVPLN) method via an integrated nested Laplace approximation (INLA) approach to investigate the dependency and the correlation between two responses (on-ramp- and merge-related crash frequencies). Models are developed for total crashes (KABCO) and fatal and severe injury crashes (KAB), utilizing 70% of a total 239 and 238 for joint on-ramp and merge segments at a.m. and p.m. peaks, respectively, from three states of a freeway (i.e., Florida, Virginia, and Wisconsin). The traffic and specific geometric data (e.g., the number of lanes, ramp configurations, presence of weaving segment, and interchange connector type) for ramp and merge segments were used as independent variables. The significant variables were found to be the exposure parameters and various geometric feature variables for ramp and merge segments. Results of posterior means for the correlation coefficients between the ramp and merge crash frequencies indicate that a significant correlation exists between the two locations.